code: def _generate_linear_forget_weights(
      self, number_samples: int
  ) -> np.ndarray:
      # From the TPE article: TPE substitutes an equally-weighted mixture of that prior with Gaussians centered at each observations.
  
      if (
          self.use_linear_forgetting_weights
          and number_samples
          >= self.number_recent_trials_at_full_weights
      ):
          weights_ramp = np.linspace(
              start=1.0 / number_samples,
              stop=1.0,
              num=number_samples
              - self.number_recent_trials_at_full_weights,
          )
          weights_flat = np.ones(
              self.number_recent_trials_at_full_weights
          )
  
          distribution_amplitudes = np.concatenate(
              (weights_ramp, weights_flat), axis=0
          )
      else:
          distribution_amplitudes = np.ones(number_samples)
  
      return distribution_amplitudes
  
imports: from collections import Counter
  from operator import itemgetter
  from typing import Any, List, Tuple, Union
  import numpy
  from neuraxle.hyperparams.distributions import Choice, DiscreteHyperparameterDistribution, DistributionMixture, HPSampledValue, HyperparameterDistribution, LogSpaceDistributionMixin, PriorityChoice, Quantized
  from neuraxle.hyperparams.space import HyperparameterSamples, HyperparameterSpace
  from neuraxle.metaopt.data.reporting import RoundReport, TrialReport
  from neuraxle.metaopt.optimizer import BaseHyperparameterOptimizer, GridExplorationSampler
label_desc: Use image node_id 9 for calling the _DividedMixturesFactory obj's underlying member method code with example usage: obj._generate_linear_forget_weights(number_samples) and returns: distribution_amplitudes